双参数流图学习用于连续时间微分同胚图像配准
Two-Parameter Flow Map Learning for Continuous-Time Diffeomorphic Image Registration
- University of Alberta（阿尔伯塔大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出双参数流图学习框架，直接学习非自治ODE的连续时间解，无需离散化，在九个数据集上提升配准精度并保持微分同胚性。
AI中文摘要：
微分同胚图像配准是医学图像分析的核心，能够实现跨受试者的解剖学一致对齐。大多数基于学习的微分同胚方法通过参数化平稳速度场并利用缩放-平方（scaling-and-squaring）方法恢复形变，从而对自治常微分方程（ODE）进行建模。虽然具有时变速度的非自治常微分方程增强了表达能力，但现有方法依赖数值积分来隐式强制流结构，这使模型表达能力与离散化精度相互纠缠。我们提出一个框架，直接学习非自治常微分方程的连续时间解，该解被表述为双参数流图。通过强制余循环一致性（cocycle consistency）——时变流的一个基本结构性质，我们在训练过程中无需时间离散化和速度积分即可学习流图。该框架在推理时通过少量组合恢复微分同胚映射。我们提出的框架无缝集成了标准配准骨干网络，并在九个数据集上持续提高对齐精度，同时保持微分同胚结构。值得注意的是，所提方法在脑部MRI基准上实现了平均Dice提升2.1%，在肺部CT上TRE降低12%，在心脏MRI和超声数据集上Dice提升2.6%。
英文摘要：
Diffeomorphic image registration is central to medical image analysis, enabling anatomically consistent alignment across subjects. Most learning-based diffeomorphic methods model autonomous ODEs(ordinary differential equations) by parameterizing a stationary velocity field and recovering deformations via scaling-and-squaring. While non-autonomous ODEs with time-dependent velocities increase expressiveness, existing approaches rely on numerical integration to implicitly enforce flow structure that entangles model expressiveness with discretization accuracy. We propose a framework to directly learn the continuous-time solution of a non-autonomous ODE formulated as a two-parameterflow map. By enforcing cocycle consistency, a fundamental structural property of time-varying flows, we learn the flow maps without time discretization and velocity integration during training. The framework recovers diffeomorphic mappings at inference using a small number of compositions. Our proposed framework seamlessly incorporates standard registration backbones and improves alignment accuracy consistently across nine datasets while preserving diffeomorphic structure. Notably, the proposed method achieves an average Dice improvement of 2.1% on brain MRI benchmarks, a 12% TRE reduction on lung CT, and a 2.6% Dice gain on cardiac MRI and ultrasound datasets.
